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Add Docs glossary links (#16448)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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6
docs/en/modes/track.md
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@ -12,9 +12,9 @@ Object tracking in the realm of video analytics is a critical task that not only
## Why Choose Ultralytics YOLO for Object Tracking?
The output from Ultralytics trackers is consistent with standard object detection but has the added value of object IDs. This makes it easy to track objects in video streams and perform subsequent analytics. Here's why you should consider using Ultralytics YOLO for your object tracking needs:
The output from Ultralytics trackers is consistent with standard [object detection](https://www.ultralytics.com/glossary/object-detection) but has the added value of object IDs. This makes it easy to track objects in video streams and perform subsequent analytics. Here's why you should consider using Ultralytics YOLO for your object tracking needs:
- **Efficiency:** Process video streams in real-time without compromising accuracy.
- **Efficiency:** Process video streams in real-time without compromising [accuracy](https://www.ultralytics.com/glossary/accuracy).
- **Flexibility:** Supports multiple tracking algorithms and configurations.
- **Ease of Use:** Simple Python API and CLI options for quick integration and deployment.
- **Customizability:** Easy to use with custom trained YOLO models, allowing integration into domain-specific applications.
@ -153,7 +153,7 @@ For a comprehensive list of tracking arguments, refer to the [ultralytics/cfg/tr
### Persisting Tracks Loop
Here is a Python script using OpenCV (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker that the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image.
Here is a Python script using [OpenCV](https://www.ultralytics.com/glossary/opencv) (`cv2`) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (`opencv-python` and `ultralytics`). The `persist=True` argument tells the tracker that the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image.
!!! example "Streaming for-loop with tracking"